Abstract

We propose a new idea for life-long mobile robot spatio-temporal exploration of dynamic environments. Our method assumes that the world is subject to perpetual change, which adds an extra, temporal dimension to the explored space and makes the exploration task a never-ending data-gathering process. To create and maintain a spatio-temporal model of a dynamic environment, the robot has to determine not only where, but also when to perform observations. We address the problem by application of information-theoretic exploration to world representations that model the uncertainty of environment states as probabilistic functions of time.

We compare the performance of different exploration strategies and temporal models on real-world data gathered over the course of several months and show that combination of dynamic environment representations with information-gain exploration principles allows to create and maintain up-to-date models of constantly changing environments.